Overview
This R package implements several non-parametric tests in chapters 1-5 of Higgins (2004), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with Rcpp for efficiency and R6 for flexible, object-oriented design, it provides a unified framework for performing or creating custom permutation tests.
Installation
Install the stable version from CRAN:
install.packages("LearnNonparam")Install the development version from Github:
# install.packages("remotes")
remotes::install_github("qddyy/LearnNonparam")Usage
library(LearnNonparam)
options(LearnNonparam.pmt_progress = TRUE)-
Construct a test object
- from some R6 class directly
t <- Wilcoxon$new(n_permu = 1e6)- using the
pmt(permutation test) wrapper
# recommended for a unified API t <- pmt("twosample.wilcoxon", n_permu = 1e6) -
Provide it with samples
-
Check the results
t$statistic
t$p_value
options(digits = 3) t$print()
ggplot2::theme_set(ggplot2::theme_minimal()) t$plot(style = "ggplot2", binwidth = 1)
-
Modify some settings and observe the change
t$type <- "asymp" t$p_value
See pmts() for tests implemented in this package.
| key | class | test |
|---|---|---|
| onesample.quantile | Quantile | Quantile Test |
| onesample.cdf | CDF | Inference on Cumulative Distribution Function |
| twosample.difference | Difference | Two-Sample Test Based on Mean or Median |
| twosample.wilcoxon | Wilcoxon | Two-Sample Wilcoxon Test |
| twosample.scoresum | ScoreSum | Two-Sample Test Based on Sum of Scores |
| twosample.ansari | AnsariBradley | Ansari-Bradley Test |
| twosample.siegel | SiegelTukey | Siegel-Tukey Test |
| twosample.rmd | RatioMeanDeviance | Ratio Mean Deviance Test |
| twosample.ks | KolmogorovSmirnov | Two-Sample Kolmogorov-Smirnov Test |
| ksample.oneway | OneWay | One-Way Test for Equal Means |
| ksample.kw | KruskalWallis | Kruskal-Wallis Test |
| ksample.jt | JonckheereTerpstra | Jonckheere-Terpstra Test |
| multcomp.studentized | Studentized | Multiple Comparison Based on Studentized Statistic |
| paired.sign | Sign | Two-Sample Sign Test |
| paired.difference | PairedDifference | Paired Comparison Based on Differences |
| rcbd.oneway | RCBDOneWay | One-Way Test for Equal Means in RCBD |
| rcbd.friedman | Friedman | Friedman Test |
| rcbd.page | Page | Page Test |
| association.corr | Correlation | Test for Association Between Paired Samples |
| table.chisq | ChiSquare | Chi-Square Test on Contingency Table |
define_pmt allows users to define new permutation tests. Take the two-sample Cramér-Von Mises test as an example:
t <- define_pmt(
# this is a two-sample permutation test
inherit = "twosample",
statistic = function(x, y) {
# (optional) pre-calculate certain constants that remain invariant during permutation
n_x <- length(x)
n_y <- length(y)
F_x <- seq_len(n_x) / n_x
G_y <- seq_len(n_y) / n_y
# return a closure to calculate the test statistic
function(x, y) {
x <- sort.int(x)
y <- sort.int(y)
F <- approxfun(x, F_x, "constant", 0, 1)
G <- approxfun(y, G_y, "constant", 0, 1)
sum(c(F_x - G(x), G_y - F(y))^2)
}
},
# reject the null hypothesis when the test statistic is large
rejection = "r",
name = "Two-Sample Cramér-Von Mises Test",
alternative = "samples are from different continuous distributions"
)
t$test(rnorm(10), runif(10))$print()